EAM reformulates adjoint matching for diffusion fine-tuning with linear base drift to allow efficient deterministic sampling and closed-form adjoints while matching or exceeding prior performance.
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Thermodynamic diffusion inference at production scale is shown using hierarchical bilinear coupling for U-Net skips and a 2,560-parameter digital bottleneck, attaining 0.9906 cosine similarity with theoretical 10^7x energy reduction over GPU.
DFP is a one-step generative policy using Wasserstein gradient flow on a drifting model backbone, with a top-K behavior cloning surrogate, that reaches SOTA on Robomimic and OGBench manipulation tasks.
Stable Audio 3 develops fast latent diffusion models for variable-length audio generation and editing via a semantic-acoustic autoencoder and adversarial post-training.
citing papers explorer
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Efficient Adjoint Matching for Fine-tuning Diffusion Models
EAM reformulates adjoint matching for diffusion fine-tuning with linear base drift to allow efficient deterministic sampling and closed-form adjoints while matching or exceeding prior performance.
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Thermodynamic Diffusion Inference with Minimal Digital Conditioning
Thermodynamic diffusion inference at production scale is shown using hierarchical bilinear coupling for U-Net skips and a 2,560-parameter digital bottleneck, attaining 0.9906 cosine similarity with theoretical 10^7x energy reduction over GPU.
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Drifting Field Policy: A One-Step Generative Policy via Wasserstein Gradient Flow
DFP is a one-step generative policy using Wasserstein gradient flow on a drifting model backbone, with a top-K behavior cloning surrogate, that reaches SOTA on Robomimic and OGBench manipulation tasks.
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Stable Audio 3
Stable Audio 3 develops fast latent diffusion models for variable-length audio generation and editing via a semantic-acoustic autoencoder and adversarial post-training.